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World Congress 2024 • Aug 20, 2024 • Session details

The AI Security Survival Guide: Practical Advice for Stressed-Out Developers

Mackenzie

Attackers are publishing malware using fake dependency names hallucinated by AI assistants. Discover how to defeat prompt injection and safely secure your automated development workflow.

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#1 about 2 min

Artificial intelligence components in the software supply chain

Integrating language models introduces inherited vulnerabilities into development workflows regardless of direct implementation.

#2 about 3 min

Differentiating language model types for software development

Understanding the safety differences between baseline-tuned completion plugins and instruction-tuned conversational agents informs developer usage.

#3 about 3 min

Evaluating training datasets and open source code quality

Models trained on vast public repositories often ingest and reproduce insecure open source coding patterns.

#4 about 4 min

Identifying and defining prompt injection execution vulnerabilities

Malicious language input bypasses instructions by treating natural language text fields as hidden executable commands.

#5 about 3 min

Demonstrating interactive prompt injection exploits in applications

Practical terminal demonstrations show how direct commands override application-specific master prompts to execute unverified actions.

#6 about 3 min

Exploiting automated email assistant integrations with malicious input

Injecting deceptive commands into external communications manipulates automated assistants into executing unverified internal actions.

#7 about 2 min

Mitigating prompt injection through architectural privilege separation

Implementing third-party security agents and dual-model quarantine systems isolates sensitive operations from untrusted language inputs.

#8 about 5 min

Exploiting artificial intelligence package hallucinations through squatting

Attackers publish malware under the names of non-existent packages frequently fabricated by language models responding to generic prompts.

#9 about 2 min

Securing applications against hallucinated developer dependency attacks

Verifying package legitimacy and utilizing software composition analysis prevents the integration of malicious hallucinated code iterations.

#10 about 4 min

Leveraging purpose-built models for escalated security attacks

Unrestricted malicious models enable attackers to refactor obsolete architectural exploits and automate highly personalized developer spear phishing campaigns.

#11 about 1 min

Defending internal networks against intelligence-augmented phishing threats

Enforcing strict access control and network segmentation limits the blast radius of developer accounts compromised via targeted phishing.

#12 about 2 min

Preventing sensitive data leakage into public language models

Training models on proprietary configuration code creates interactive avenues for extracting hardcoded credentials and internal infrastructure secrets.

#13 about 2 min

Integrating generative models safely without outright prohibition

Banning corporate models encourages risky shadow usage, making developer education and foundational environment security practices essential components of adoption.

Matching moments

3:00 min

Top security vulnerabilities for AI applications

Deepu Deepu · WWC 2025

2:01 min

The necessity of developer intelligence amidst automated attack generation

Julian Totzek-Hallhuber Julian Totzek-Hallhuber · WWC 2024

4:15 min

Security integration and AI skepticism in developer tooling

Chris Heilmann +2 · LIVE

5:18 min

Addressing psychological safety and ethical risks of AI adoption

Vera Slavnić Vera Slavnić · Europe 2026 Virtual

3:37 min

Managing security risks in AI-accelerated development processes

Carey Liu Carey Liu · WWC Europe 2026

2:31 min

Emerging risks and attack vectors in AI systems

Matteo Meucci Matteo Meucci · Europe 2026 Virtual

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